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For a blnomial experiment with 20 trials AX < 4) - PX > 161.TrucFalse...

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For a blnomial experiment with 20 trials AX < 4) - PX > 161.TrucFalse

For a blnomial experiment with 20 trials AX < 4) - PX > 161. Truc False



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For Exercises 5 through $20,$ assume that the variables are normally or approximately normally distributed. Use the traditional method of hypothesis testing unless otherwise specified. Soda Bottle Content A machine fills 12 -ounce bottles with soda. For the machine to function properly, the standard deviation of the population must be less than or equal to 0.03 ounce. A random sample of 8 bottles is selected, and the number of ounces of soda in each bottle is given. At $\alpha=0.05,$ can we reject the claim that the machine is functioning properly? Use the $P$ -value method. $$ \begin{array}{cccc}{12.03} & {12.10} & {12.02} & {11.98} \\ {12.00} & {12.05} & {11.97} & {11.99}\end{array} $$

Problem 33. We want to see if the following statement is true or false. The statement says, for the probability density function B of X. If be often equals B of 20 then none of the population has X values blowing between then and 20 course this statement is false because this is the definition of the community distribution function, not the probability density function. Because we can have an example of the probability dynasty function with values like this and go something like that and then get up again and to be something rather, we can see that if this is 10 and this is 20 the M they have the same value and the values in between 10 and 20 has values for a value for 15 years. We can have a value for the population and for these 12 for example, and for 18 or 17 for example, we can have a value and so on. The X values between 10 and 20 has values of P of X. And the statement is false.

In problem 34. We want to see if the following statement is true or false. The statement says for a cumulative distribution function B of X. If p often equals B of 20 then none of the population has X values lying between 10 and 20. This statement is true because this is the definition of the cumulative distribution function. It's increasing function or non decreasing function at least. And let's make this clear. Using a graph. We know that the cumulative distribution function is a non decreasing function. What she means if it has a constant value between two points, for example, 10 and 20 then the values in between should be honest. It can't B like this because it's non decreasing and can be this because it's non decreased, then A B of 10 equals B of 20. This means the value in between equal as an equal value, which is constant value here. And this makes the values of the population equals zero in between. Because B of 20 by definition equals the integration from 10 to 20. For that dynasty function B. Of X. Dx plus be often because it's a cumulative we take the often and added the integration from 10 to 20 it's committed. And here we have B. Of 20 equals B. Of 10. This makes this integration equal zero. Then we can conclude that the dynasty function equals zero between for X between 10 and the 20. And this is the definition of the statement. The revolution has no values blowing between 10 and 20. Then again the statement is true.

We want to evaluate whether or not the following sentiment is true or false. To test. H not mu equals you not against alternative hypothesis. H a mute does not equal unit at 5% significance. We would construct a 95% confidence in general. This question is beyond the understanding of the different approaches the hypothesis testing, namely it's testing our understanding of the confidence interval approach. So remember that in the confidence interval approach we construct a one minus alpha times 100% confidence interval. So the interval is a value one minus alpha times 100%. Remember that alpha is simply your confidence level of significance. So for alpha equals 1000.5 we're going to be constructing a one minus 10.5 times 100% equals 95% confidence interval. So in our testing, H not versus A J, we would be constructing this 95% confidence interval, which means that the question rather the answer to this question is true.

So if the P value in a statistical test is greater than the level of significance for the test, We actually failed to reject anil hypothesis, meaning if my alpha value is 0.05, and if my p value Comes out to be let's say 0.06, my p value will be greater than alpha, and hence I will fail to reject final hypothesis. Why is this? What exactly is fee value? P value is the probability that I am observing this by chance, right? P value means the value is basically the chance of getting something that you already have or something rarer than that. Okay, so I will fail to reject minor hypothesis, fail to reject my nail hypothesis. H not? Oh!


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